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Found 12 papers, 1 papers with code
An Adversarial Approach to Evaluating the Robustness of Event Identification Models
Thorough experiments on the synthetic South Carolina 500-bus system highlight that a relatively simpler model such as logistic regression is more susceptible to adversarial attacks than gradient boosting.
A Semi-Supervised Approach for Power System Event Identification
Using this package, we generate and evaluate eventful PMU data for the South Carolina synthetic network.
Robust Model Selection of Non Tree-Structured Gaussian Graphical Models
A recent line of work establishes that even for tree-structured graphical models, only partial structure recovery is possible and goes on to devise algorithms to identify the structure up to an (unavoidable) equivalence class of trees.
The Saddle-Point Accountant for Differential Privacy
SPA approximates privacy guarantees for the composition of DP mechanisms in an accurate and fast manner.
Parameter Estimation in Ill-conditioned Low-inertia Power Systems
This ill-conditioning is because of converter-interfaced power systems generators' zero or small inertia contribution.
Cactus Mechanisms: Optimal Differential Privacy Mechanisms in the Large-Composition Regime
Since the optimization problem is infinite dimensional, it cannot be solved directly; nevertheless, we quantize the problem to derive near-optimal additive mechanisms that we call "cactus mechanisms" due to their shape.
A Machine Learning Framework for Event Identification via Modal Analysis of PMU Data
Using the obtained subset of features, we investigate the performance of two well-known classification models, namely, logistic regression (LR) and support vector machines (SVM) to identify generation loss and line trip events in two datasets.
Generation of Synthetic Multi-Resolution Time Series Load Data
The availability of large datasets is crucial for the development of new power system applications and tools; unfortunately, very few are publicly and freely available.
Synthetic Time-Series Load Data via Conditional Generative Adversarial Networks
A framework for the generation of synthetic time-series transmission-level load data is presented.
A Verifiable Framework for Cyber-Physical Attacks and Countermeasures in a Resilient Electric Power Grid
In this paper, we investigate the feasibility and physical consequences of cyber attacks against energy management systems (EMS).
Three Variants of Differential Privacy: Lossless Conversion and Applications
In the first part, we develop a machinery for optimally relating approximate DP to RDP based on the joint range of two $f$-divergences that underlie the approximate DP and RDP.
A Better Bound Gives a Hundred Rounds: Enhanced Privacy Guarantees via $f$-Divergences
We derive the optimal differential privacy (DP) parameters of a mechanism that satisfies a given level of R\'enyi differential privacy (RDP).
